Step 1: Provide data

• Data should be provided for only one chemical at a time.
• Each species should not have more than one concentration value.
• Data must have at least one column containing at least 8 distinct, positive, non-missing concentration values.
• Optionally, species and group columns can be included, which are used to label and color plot output, respectively.

• Any additional columns are accepted but are not used by any functions.

There are three options to provide data to the app:

1. Use the demo Boron dataset.
   • Quickly preview the app functionality on a dataset that ‘works’.
2. Upload a csv file.
   • Excel file formats are not accepted. If you have an excel file, export a worksheet to csv.
3. Fill out the interactive table.
   • Click on a cell to begin entering data. Right-click on the table to delete/insert rows or columns. Column names cannot be changed.

Finally, preview the data provided in the table on the right hand side of the tab.

Provide data in tab 1. Data

Step 2: Fit distributions

1. Specify which column contains concentration values. The app attempts to guess which column contains concentration values based on data column names. This may need to be corrected.
2. Select or deselect distributions to fit. The outputs may take a moment to update.
3. Format the plot using inputs in the sidebar and download plot and goodness of fit table as a png and csv file, respectively.
   

Fit distributions in tab 2. Fit

Additional information about the goodness of fit table: The columns in the goodness of fit table are the distribution (dist), the Anderson-Darling statistic (ad), the Kolmogorov-Smirnov statistic (ks), the Cramer-von Mises statistic (cvm), Akaike’s Information Criterion (aic), Akaike’s Information Criterion corrected for sample size (aicc), Bayesian Information Criterion (bic), the AICc difference (delta) and the AICc based Akaike weight (weight). The prediction is the model averaged (using aicc) estimate of the fit. The percent hazard concentration is the concentration of the chemical which is predicted to affect that percent of the species tested.

Step 3: Predict hazard concentration

1. Select the threshold % species affected to calculate estimated hazard concentration. This affects the plot (dotted line), text displayed below the plot and calculations of confidence limits.
2. Select the number of bootstrap samples used to calculate confidence limits. The recommended number of samples is 10,000, although this can take around 3 minutes to process. Select lower number of bootstrap samples to reduce processing time.

3. Since confidence limits take time to calculate, they are not calculated automatically; you must press the Get CL button.
4. Format plot using various inputs in sidebar and download plot and table as png and csv file, respectively.
   • If the species labels are not fully visible within the plot, adjust the x-axis max limit and label position using inputs labeled ‘X-axis max’ and ‘Adjust label’, respectively.
   • Qualitative colour palettes are from ColorBrewer
   • If you are using the same variable for colour and shape, providing the same legend title will combine both into a single legend.

ColorBrewer qualitative palettes.

Get hazard concentration estimates and confidence limits in tab 3. Predict

Step 4: Get R code

Copy R code to reproduce outputs programmatically. Code is dynamically generated based on user inputs and functions exectured within the app (e.g., code for generating confidence limits will appear after ‘Get CL’ button is clicked).

Get R code to reproduce results programmatically in tab R Code